Abstract

The coupled substitution between Na+Si and Ca+Al, in the plagioclase solid solution, results in a continuous variation in the Al/Si ratio of the composition, which is the reason for the complicated ordering patterns in the intermediate plagioclase feldspars such as labradorite. Both fast-cooled and slow-cooled labradorite feldspars display the incommensurately modulated structures. The ordering pattern in the incommensurately modulated structures of e-plagioclase (characterized by the satellite diffraction peak called e-reflections) is the most complicated and intriguing. The modulated structure has a super-space group symmetry of X(αβγ)0, with a special centering condition of (½ ½ ½ 0), (0 0 ½ ½), (½ ½ 0 ½), and the q-vector has components (i.e., δh, δk, δl) along all three axes in reciprocal space. Displacive modulation, occupational modulation, and density modulation are observed in slowly cooled labradorite feldspars. No density modulation was observed in fast cooled (volcanic) labradorite feldspars. The amplitudes of the modulation waves are new parameters for quantifying the ordering state of labradorite. Iridescent labradorite feldspars display exsolution lamellae with an average periodicity ranging from ~150 nm to ~350 nm. Compositional difference between the lamellae is about 12 mole % in anorthite components. Areas or zones with red (or yellow) iridescent color (i.e., long lamellae periodicity) always contain more Ca (~1 to 3 mole %) than the areas with blue (or green) iridescent color within the same labradorite crystal. We proposed that the solvus for Bøggild intergrowth has a loop-like shape, ranging from ~An44 to ~An63. The Ca-rich side / zone has higher exsolution temperature than the Na-rich side / zone. The shapes of satellite peaks, the distances between e-reflections (modulation periods), and even the intensity of e-reflections may also be used to evaluate the ordering state or cooling rate of the plagioclase feldspar. Both modulated structure and the exsolution lamellae can be used as proxies for quantifying cooling rate of a labradorite and it’s host rock.

Highlights

  • Allocate a distance to each observation Robust distance → S-estimator Statistical mean of the data Computing the covariance matrix

  • The mentioned process, applied to the oxide zone of Sarigunay epithermal gold deposit in Iran, results in 286 data points detected as outliers through an 11945 sampled dataset in which the ratio of outliers to raw data is 2.39%

  • Combination of statistical and geostatistical outlier detection methods leads to robust variograms and more precise estimation

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Summary

Introduction

Simin Saadati1, Mohammad Fahimi Nia 2, and Omid Asghari 3,* Abstract: Statistically, outliers are the data remarkably dissimilar to the whole dataset. These existing outliers may give rise to misinterpretations in statistical and geostatistical analyses. To detect outliers two methods of (1) boxplot as a representative of statistical methods and (2) a combination of Mahalanobis Distance (MD) and network graph as a representative of geostatistical methods are applied.

Results
Conclusion

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